Intuition Machine
Understand the enterprise. Deploy AI within it. Prove that it works.
Most AI pilots succeed in isolation and stall at the boundary of the real organisation. We work from the other end: start with the rules, systems and dependencies the AI has to live inside, and build outward from there.
Understand what it takes to deploy AI
Before anything is automated, we find out what the work actually depends on: which systems it touches, which policies govern it, who holds which decision, and where the written process and the real one have drifted apart.
- Enterprise rules, approved technologies and the constraints that apply to them
- Dependencies between systems, teams and decisions
- Conflicts between rules, surfaced before an agent trips over them
- Workflows that are good candidates for automation, and ones that are not
Underneath: an enterprise context graph, semantic analysis and capability mapping
Turn AI experiments into enterprise-ready workflows
We build narrow, purpose-made execution environments for AI agents: each one knows the systems it may use, the identity it acts under, the approvals it needs and the standards it has to deploy against.
- Integration with the enterprise applications the workflow already runs on
- Identity, permissions and approvals enforced, not merely documented
- Work routed to the right handler, with a human path held open
- Deployment against your existing architecture and standards
Underneath: generated agent environments, agent orchestration and typed routing
Show it delivers value and stays within its boundaries
An AI workflow earns more autonomy by producing evidence, not by passing a demo. We measure it against what actually happened, enforce its policies continuously, and flag the moment its behaviour starts to drift.
- Outcome evaluations against the workflow it replaces
- Policy enforcement, audit trails and escalation
- Autonomy that expands only as the evidence supports it
- Drift detection, so degradation is caught rather than reported around
Underneath: judgment governance, bounded judgments, continuous evaluation and capability promotion criteria
Your enterprise has rules. Your AI should know them.
Compliance is the smallest part of it. The rules of an organisation are its architecture, its roles, its operating procedures, its economic priorities and the ways its departments coordinate. An agent that ignores any of them is a liability, however capable it is.
From experiment, to dependable operation, to reusable capability
Capabilities evolve. What starts as an experiment becomes a dependable part of operations, and eventually a utility other workflows build on. We manage that whole progression, so each workflow you prove makes the next one cheaper.
Find out what works
Cheap trials with honest results, run in shadow alongside the existing process.
Make it dependable
Governed, evaluated and integrated, with autonomy matched to the evidence.
Make it reusable
The proven capability becomes infrastructure the next workflow starts from.
Start with one workflow.
We map your constraints, build a governed AI workflow alongside your existing operations, and prove the results before autonomy expands.